How Do Regulated Enterprises Deploy Private, Secure AI?

Aaron (0:08)

Good morning, good evening, wherever you are, and welcome back to the Enterprise AI Show.

This is your host, Aaron, and super quick intro today. We have Ivan Lee, founder and CEO of Datasaur, to talk about what it takes to build private, secure AI for regulated enterprises. We talk a bit about the buy versus rent debate and dig into control and ownership of AI and how that is critical to the enterprise. And all of that is coming up right after this quick break What does the outside world already know about your company?

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Aaron (2:16)

And we're back, and for today's topic, it's actually something Brian and I have talked about on the podcast a n- a number of times now, but enterprise AI, but more specifically secure and sovereign enterprise AI.

And for that, we have Ivan Lee. Ivan is the founder and CEO of Datasaur. Am I saying that correctly, first of all, Ivan?

Ivan Lee (2:41)

That's correct. Thanks

Aaron (2:43)

cool. I just wanna make sure. And what we're gonna do is we're gonna jump into a bunch of topics here, but before we do that, why don't you give us, Ivan, a little bit about your background? You've got a really interesting background from, you had an early exit under your belt and then went over to Apple and now kinda doing your own thing. But, I jokingly refer to everyone that's been doing, AI before LLMs, you did AI before it was cool.

So give everyone a little bit about the background

Ivan Lee (3:10)

absolutely. My name is Ivan. I'm the founder and CEO here at Datastore.ai. My own background is that I studied computer science at Stanford. So fun fact my very first AI class was with Dr. Andrew Ng, and my first computer vision class was with Dr. Fei-Fei Li.

And so I got to learn from these global superstars before, their stardom. As you mentioned, my first company was a very different space. It was mobile gaming. Ended up selling that to Yahoo where I began working on machine learning for the first time. This is 2013, the last AI wave.

And I worked on search and machine learning algorithms at Yahoo, then Apple before realizing that I really enjoyed the world of NLP, natural language processing, and decided to found this company.

Aaron (3:58)

Very nice. And tell everyone a little bit about, And we'll talk about enterprise AI here in a second, but I'm just really fascinated about the background here.

What was it like doing NLP before NLP really was a big part of generative AI, right? Like what was the fit and what was the use case, and why were people doing, NLP and even data labeling as well back then?

Ivan Lee (4:23)

Yeah. I think it was just this pursuit of being able to teach machines about our language and how we operate as society. And it felt like such this such a fundamental part of how we communicate, and it was just really fun watching machines learn more about us and be able to communicate.

So I worked on products like Siri and it felt like we were on the cusp of something, but even I could not have foreseen the, big ChatGPT moment.

Aaron (4:51)

That's fair. That's fair. All right, so let's jump in here.

So what I want, what I, I wanna do is start at a high level, and then we'll kinda keep going lower with each of these topics here. Let's kinda start with data sovereignty and secure AI. The reason why I wanna start here is in particular we tend to be a little m- less timely on the podcast. We don't like to introduce all the news because it tends to, really okay, it might age the podcast a little quickly, especially with the speed of AI news these days. But there has been recently the whole idea of, I'm gonna use the, the term forced training, meaning, Fable and some of the others where it's like, okay, you-- there's no opting out of training the data. And I've seen that as one big aspect for data sovereignty, and certainly has made this almost like a, a board level and C-level issue extremely quickly.